Adaptive reuse of former hospital sites in Ontario: lessons learned from the planning process
Bibliographic record
Abstract
Since the 1990s, Ontario’s health care system has faced a number of changes with respect to increasing expenses not reflected in the allotted funding for hospitals. The restructuring of Ontario’s hospital landscape has resulted in amalgamations, takeovers and closures leaving behind viable surplus hospital sites. This paper focuses on the municipal planning process of adaptive reuse through the lens of former hospitals sites in Ontario. The opportunities and challenges that currently exist in the planning process are examined through four case studies of former hospital sites: Sault Area Hospital in Sault Ste. Marie, St. Catharines General Hospital in St. Catharines, St. Joseph's Hospital in Sudbury and St. Joseph’s Hospital in Peterborough. The findings are summarized in to a set of lessons learned from the planning process. These lessons can be used by municipalities to enhance the overall planning process for these former institutional buildings
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.019 | 0.011 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".